Merge pull request #62 from watson-developer-cloud/alchemy-async

Alchemy async

.gitignore

@@ -4,3 +4,5 @@ uploads/*
 !uploads/README.md
 VCAP_SERVICES.json
 run.sh
+
+.env.js

README.md

@@ -1,6 +1,6 @@
 # Visual Recognition Nodejs Starter Application
 
-The [IBM Watson Visual Recognition service][visual_recognition_service] analyzes the visual content of images to understand the scene without any input text describing
+[Visual Recognition][visual_recognition_service] partnered with [Alchemy Vision](www.alchemyapi.com/products/alchemyvision), allows you to derive insights from an image based on its visual content. You can organize image libraries, understand an individual image, and create custom classifiers for specific results that are tailored to your needs.
 
 Give it a try! Click the button below to fork into IBM DevOps Services and deploy your own copy of this application on Bluemix.
 
@@ -37,21 +37,26 @@ applications:
   $ cf create-service visual_recognition free visual-recognition-service
   ```
 
-6. Push it live!
+6. Create the Alchemy service in Bluemix or copy your existing key into `ALCHEMY_KEY` in `app.js`
+  ```sh
+  $ cf create-service alchemy_api free alchemy-service
+  ```
+
+7. Push it live!
   ```sh
   $ cf push
   ```
-  
+
 7. Optional Security and Performance enhancements
 
   Set `NODE_ENV=production` to enable view caching and other performance enhancements:
-  
+
   ```sh
   $ cf set-env <application-name> NODE_ENV production
   ```
-  
+
   Set `SECURE_EXPRESS=1` to enable rate-limiting, HTTPS-only, and several other security features:
-  
+
   ```sh
   $ cf set-env <application-name> SECURE_EXPRESS 1
   ```

app.js

@@ -16,6 +16,17 @@
 
 'use strict';
 
+try {
+  var env = require('./.env.js');
+  console.log('loading .env.js');
+  for (var key in env) {
+    if (!(key in process.env))
+      process.env[key] = env[key];
+  }
+} catch(ex) {
+  console.log('.env.js not found');
+}
+
 var express = require('express'),
   app = express(),
   fs = require('fs'),
@@ -26,6 +37,7 @@ var express = require('express'),
   request = require('request'),
   datasets = require('./public/data/datasets.json'),
   zipUtils = require('./config/zip-utils'),
+  uuid      = require('uuid'),
   watson = require('watson-developer-cloud');
 
 
@@ -42,8 +54,24 @@ var visualRecognition = watson.visual_recognition({
   version_date:'2015-12-02'
 });
 
+var alchemyVision = watson.alchemy_vision({
+  api_key: process.env.ALCHEMY_KEY || '<alchemy-key>'
+});
+
 app.get('/', function(req, res) {
-  res.render('index', datasets);
+  res.render('use', datasets);
+});
+
+app.get('/use', function(req, res) {
+  res.render('use', datasets);
+});
+
+app.get('/train', function(req, res) {
+  res.render('train', datasets);
+});
+
+app.get('/test', function(req, res) {
+  res.render('test', datasets);
 });
 
 /**
@@ -67,6 +95,36 @@ function filterUserCreatedClassifier(result, classifier_ids) {
   }
   return result;
 }
+
+/**
+ * Normalize Alchemy Vision results
+ * @param  {Object} Alchemy vision result
+ * @return {Object} Visual Recognition result
+ */
+function normalizeResult(item) {
+  var result = {
+    name: item.text || 'Unknown',
+    score: parseFloat(item.score || '0')
+  };
+  return result;
+}
+
+function noTags(tag) {
+  return tag.name !== 'NO_TAGS';
+}
+/**
+ * Formats Alchemy Vision results to match the Watson Vision format
+ * @param  {Object} result        The result of calling 'classify()'
+ * @return {Object}               The formatted 'result'
+ */
+function formatAlchemyVisionResults(results) {
+  return {
+    images: [{
+      scores: results.imageKeywords.map(normalizeResult).filter(noTags)
+    }]
+  };
+}
+
 /**
  * Creates a classifier
  * @param req.body.positives Array of base64 or relative images
@@ -138,6 +196,12 @@ app.post('/api/classify', app.upload.single('images_file'), function(req, res, n
   if (req.file) {
     // file image
     file = fs.createReadStream(req.file.path);
+  } else if (req.body.image_data) {
+    // write the base64 image to a temp file
+    var resource = zipUtils.parseBase64Image(req.body.image_data);
+    var temp = './uploads/' + uuid.v1() + '.' + resource.type;
+    fs.writeFileSync(temp, resource.data);
+    file = fs.createReadStream(temp);
   } else if (req.body.url && validator.isURL(req.body.url)) {
     // web image
     file = request(req.body.url.split('?')[0]);
@@ -149,23 +213,33 @@ app.post('/api/classify', app.upload.single('images_file'), function(req, res, n
     return next({ error: 'Malformed URL', code: 400 });
   }
 
-  var params = {
-    images_file: file
-  };
+  if (req.query.classifier_id) {
+    var vparams = {
+      images_file: file,
+      classifier_ids: [req.query.classifier_id]
+    };
 
-  if (req.query.classifier_id)
-    params.classifier_ids = [req.query.classifier_id];
+    visualRecognition.classify(vparams, function(err, results) {
+      if (req.file || req.body.image_data) // delete the recognized file
+        fs.unlink(file.path);
 
-  visualRecognition.classify(params, function(err, results) {
-    // delete the recognized file
-    if (req.file)
-      fs.unlink(file.path);
-
-    if (err)
-      return next(err);
-    else
-      res.json(filterUserCreatedClassifier(results, params.classifier_ids));
-  });
+      if (err)
+        return next(err);
+      else
+        res.json(filterUserCreatedClassifier(results, vparams.classifier_ids));
+    });
+  } else {
+    alchemyVision.getImageKeywords({ image: file}, function (err, results) {
+      // delete the recognized file
+      if (req.file || req.body.image_data)
+        fs.unlink(file.path);
+
+      if (err)
+        return next(err);
+      else
+        res.json(formatAlchemyVisionResults(results));
+    });
+  }
 });
 
 // error-handler settings

config/security.js

@@ -28,7 +28,7 @@ module.exports = function (app) {
   app.use(secure());
 
   // 2. helmet with defaults
-  app.use(helmet());
+  app.use(helmet({ cacheControl: false }));
 
   // 3. rate-limit to /api/
   app.use('/api/', rateLimit({

config/zip-utils.js

@@ -38,6 +38,7 @@ function parseBase64Image(imageString) {
   resource.data = new Buffer(matches[2], 'base64');
   return resource;
 }
+module.exports.parseBase64Image = parseBase64Image;
 
 /**
  * Archives an image uning the @param archive module
@@ -61,7 +62,6 @@ function archiveImage(archive, image) {
   }
 }
 
-
 /**
  * Creates a zip file with the images array
  * @param images The images to zip

package.json

@@ -29,19 +29,19 @@
     "start": "node app.js"
   },
   "dependencies": {
-    "archiver": "^0.16.0",
+    "archiver": "^0.21.0",
     "async": "^1.5.0",
-    "body-parser": "^1.13.2",
-    "express": "^4.12.2",
-    "express-rate-limit": "^2.0.2",
+    "body-parser": "^1.15.0",
+    "express": "^4.13.4",
+    "express-rate-limit": "^2.1.0",
     "express-secure-only": "^0.2.1",
-    "find-remove": "^0.2.8",
+    "find-remove": "^0.2.10",
     "helmet": "^1.3.0",
     "jade": "^1.11.0",
     "multer": "^1.1.0",
-    "request": "^2.65.0",
+    "request": "^2.69.0",
     "uuid": "^2.0.1",
-    "validator": "^4.7.0",
-    "watson-developer-cloud": "^1.0.6"
+    "validator": "^5.1.0",
+    "watson-developer-cloud": "^1.3.1"
   }
 }